In recent years, neural networks have been widely applied to solve PDEs. Physics-informed neural networks (PINNs) [1]} based on multi-layer perceptron (MLP) models approximate solutions by the optimization of a loss function consisting of given physics laws. The biggest benefit of PINNs is that solutions can be inferred without any iterative process such as a recurrence equation with respect to time. Furthermore, it is used for diverse applications such as Hidden Fluid Mechanics [2]} that extracts hidden variables of a given equation using a PINN and observations. However, it is hard to optimize model parameters when we deal with complicated PDEs and their coefficients. In order to improve the training ability, combinations of PINNs and numerical methods have been developed, or other neural networks such as CNNs are selected. M. Raissi et al. [1]} added Runge-Kutta methods to a PINN model for solving AC equation. Aditi et al. [4]} proposed transfer learning and curriculum regularization which start training PINNs on a specific safe domain and then transfer to a target domain. Hao Ma et al. [5]} proposed a U-shape CNN so-called U-net [6]} and they showed that the usage of target data in a loss function significantly improves optimization. Elie Bretin et al. [7]} used convolutional neural networks derived from a semi-implicit approach to learn phase field mean curvature flows of the AC equation.
